Relevance weighted (2D)2LDA image projection technique for face recognition application

In this paper, a novel image projection technique for face recognition application is proposed based on the Linear Discriminant Analysis (LDA) combining with relevance weighted. The projection technique is performed through 2-directional and 2-dimensional LDA or (2D)²LDA approach which simultaneously work in row and column directions to overcome the ldquosmall sample sizerdquo problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and relevance weighted is also used in the within-class scatter matrix in order to weigh the information for solving confusable data in these classes. This technique is called the Relevance Weighted 2-Dimensional and 2-Directional LDA or RW(2D)²LDA which are used for a more accurate discriminant decision than the one that is produced by the conventional LDA, or 2DLDA. The proposed technique has been successfully tested on three face databases. Experimental results indicate that the proposed RW(2D)²LDA algorithm is more computationally efficient than the conventional algorithms in view of a little features and less times. It can also improve performance, and takes maximum recognition rate more over 97%.